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Frequent jump pattern mining based on complex network

Journal of Computational Information systems 2015年第17期11卷 6451-6458页

作者： Wang, Lei Jiang, Liya Dong, Jun Huang, Guoyan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Normal University of Science and Technology Qinhuangdao China

Many researchers are devoted to find frequent pattern in static network. Due to these frequent patterns are usually defined as frequent existence patterns which satisfy given support threshold in network. However, the temporal feature in dynamic network is ignored. In this paper, frequent jump pattern (FJP) is proposed, it not only represents frequent pattern but also can show regular behavior in evolving social network. Furthermore, jump patterns are robust against noise in the analysis of dynamical process. We construct matrix of directed graph (MDG) to present the evolution of complex directed network over time. Moreover, a frequent jump pattern searching algorithm (FJPSA) based on MDG is proposed to find dynamic evolution rule of social network. FJP mining is a technique which can be used in many application fields, such as explaining biological evolution and mining customer behavior regularity in social network. Experimental results demonstrate that our method could effectively mining FJP-graph. © 2015, by Binary Information Press

关键词： Directed graphs

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New methods of constructing quaternary sequence Pairs

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ICIC Express Letters 2016年第9期10卷 2177-2183页

作者： Jia, Yanguo Shen, Xiumin Duan, Xiaobei Song, Xiaofei School of Information Sciences and Engineering Yanshan University China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao066004 China

In this letter, new methods of constructing quaternary sequence pairs are presented based on binary sequence pairs with two-level autocorrelation, almost perfect binary sequence pairs and cyclic shift sequences. The quaternary sequence pairs con- structed by these methods are periodic autocorrelation signals. These methods are handy and exible, and can greatly enrich the theory of quaternary sequence pairs. © 2016 ICIC International.

关键词： Autocorrelation

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A Generalized Weighted Closed Sequential Pattern Mining Algorithm with Item Interval

A Generalized Weighted Closed Sequential Pattern Mining Algo...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Haitao Lu Shuo Li College of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Library of Yanshan University Yanshan University

ISBN: (纸本)9781510803084

The algorithm of this paper inserts pseudo items which are converted from item interval to obtain equal extended sequence database;it defines item-interval constraints, which are relative to the item weight, to prune the mining patterns. Through doing this, the algorithm avoids mining the patterns which users are not interested in and shortens the running time. It adopts histogram statistic pattern to get the standardization description to item interval of the mining patterns, making the mining sequences include the item interval information which is valuable to user decision.

关键词： weighted sequential patterns closed sequential patterns item interval

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Research on the Influence of Improved K-shell Algorithm on Commodity Profit

Research on the Influence of Improved K-shell Algorithm on C...

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作者： Jing Chen Jincheng Huang College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and system Integration of Hebei Province Software Engineering Laboratory in Hebei Province

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN(Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.

关键词： Influence threshold K-shell influence maximization profit

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Kainate induced gamma oscillations and spike-gamma relationship analysis

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Advances in Information Sciences and Service Sciences 2012年第21期4卷 606-612页

作者： Wen, Dong Peng, Ce Li, Xiaoli Lu, Chengbiao The Key Laboratory for Computer Virtual Technology System Integration of HeBei Province Yanshan University Qinhuangdao Hebei 066004 China Institue of Electrical Engineering Yanshan University Qinhuangdao Hebei 066004 China

Neuronal oscillations in the gamma frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of kainate in the generation of oscillatory field activity at gamma frequency in the hippocampal slice prepared from rat brain, and to determine the phase relationship between spikes and gamma oscillations. The kainate induced large amplitude field population spiking activity, which is correlated linearly with the field gamma oscillations. The relationship between timing of spikes and the phase of gamma oscillation was determined by an analysis of circular statistics. Further analysis with Rayleigh test revealed that the relationship of phase-locking between spikes and gamma rhythm is of statistical significance. These data demonstrate that kainate sensitive neuronal networks within hippocampus are able to generate gamma oscillations which are modulated by large amplitude population spikes and phase-locking of spikes to the gamma rhythm suggest the role of memory enhancement in the presence of Kainate receptor activation.

关键词： Locks (fasteners)

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A community detection method to undirected weighted signed social networks

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Journal of Computational Information systems 2015年第10期11卷 3623-3632页

作者： Guo, Jingfeng Liu, Miaomiao Liu, Linlin Liu, Yuanying College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Northeast Petroleum University Daqing China

With regard to the community mining in weighted signed networks and weighted social networks containing only positive links, the AGMA algorithm has some imperfections in the second clustering of vertices. So some improvements were made to it. And a new algorithm CD WSSN (Community Detection for Weighted Signed Social Networks) is proposed to detect communities in undirected and unweighted signed networks. It takes the sign of the edge, the weight of the edge and the friend coefficient between the vertex and community as clustering criteria of vertices. It can divide a weighted signed network into the right number of communities accurately and effectively. Experiments were done on datasets like weighted signed networks, weighted traditional social networks, unweighted signed networks as well as standard networks. The comparative analysis of the experimental results were also done with regard to the difference and frustration about CD WSSN, AGMA, CRA algorithm, and so on. The results verify the accuracy and generality of the algorithm. ©, 2015, Binary Information Press. All right reserved.

关键词： Population dynamics

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Automatic simulated computation of remote sensing information model based on semantic

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Journal of Chemical and Pharmaceutical Research 2014年第7期6卷 521-529页

作者： Sun, Shengtao Wu, Aizhi The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China College of Vehicles and Energy Yanshan University Qinhuangdao China

This paper discusses the simulated computation methods of remote sensing information model, and tries to put forward a more available solution. It presents our research works on the description and simulation methods of remote sensing information model: the service on demand method provides a basic methodology of model analysis, the semantic relationship description of information processing procedure supports the basic knowledge of resource selection and matching based on user’s demand, and the hierarchical mapping method of remote sensing information model shows the transformation from abstract model to executable data processing workflow. © 2014, Journal of Chemical and Pharmaceutical Research. All rights reserved.

关键词： Semantics

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Research on a Novel Influence Maximization Algorithm Based on Community Structure

Research on a Novel Influence Maximization Algorithm Based o...

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作者： Jing Chen Jiangchuan Liu College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Software Engineering Laboratory in Hebei Province

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.

关键词： Seed node influence maximization community division overlapping influence community structure independent cascade model

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Outlier detection based on cluster outlier factor and mutual density 10th

Outlier detection based on cluster outlier factor and mutual...

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10th International Symposium on Intelligence Computation and Applications, ISICA 2018

作者： Zhang, Zhongping Zhu, Mengfan Qiu, Jingyang Liu, Cong Zhang, Debin Qi, Jie School of Information Science and Engineering Yanshan University QinhuangdaoHebei066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei066004 China Hebei Education Examinations Authority ShijiazhuangHebei050000 China The First Middle School of Qian An Country Qian’anJilin131400 China

ISBN: (纸本)9789811364723

Outlier detection is an important task in data mining with numerous applications. Recent years, the study on outlier detection is very active, many algorithms were proposed including that based on clustering. However, most outlier detection algorithms based on clustering often need parameters, and it is very difficult to select a suitable parameter for different data set. In order to solve this problem, an outlier detection algorithm called outlier detection based on cluster outlier factor and mutual density is proposed in this paper which combining the natural neighbor search algorithm of the Natural Outlier Factor (NOF) algorithm and based on the Density and Distance Cluster (DDC) algorithm. The mutual density and γ density is used to construct decision graph. The data points with γ density anomalously large in decision graph are treated as cluster centers. This algorithm detect the boundary of outlier cluster using cluster outlier factor called Cluster Outlier Factor (COF), it can automatic find the parameter. This method can achieve good performance in clustering and outlier detection which be shown in the experiments. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Data mining

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A sketch recognition method based on bi-modal model using cooperative learning paradigm

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Neural Computing and Applications 2024年第23期36卷 14275-14290页

作者： Zhang, Shihui Wang, Lei Cui, Zhiguo Wang, Shi School of Information Science and Engineering Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China School of Information Technology Hebei University of Business and Economics Xuefu Road Hebei Province Shijiazhuang050061 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Street West Section Hebei Province Qinhuangdao066000 China

Static image is an important form of displaying a sketch, representing the appearance information of the sketch. And a stroke sequence composed of several points can also express the shape and contour information of the sketch. Therefore, it is very reasonable to treat a sketch as point-modal data and image-modal data simultaneously. In this paper, a method based on bi-modal model using cooperative learning paradigm is proposed for the sketch recognition task. Specifically, in the point-modal branch, a structural point convolution block is developed by properly dividing local regions to preserve the structural information. In the image-modal branch, the hierarchical residual structure is used to fully extract image-modal features. To reduce the negative impact of noisy samples on the recognition performance, a cooperative learning paradigm is designed based on different perceptual abilities of two modal branches on noisy samples, that is, when training the two branches, the noisy samples can be filtered out through information exchanges and mutual learning. Extensive experiments on the sketch datasets TU-Berlin and QuickDraw show that the proposed method outperforms most baseline methods and has many advantages such as no dependence on additional data and stroke information. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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